Podsumowanie

Dane wybrane do predykcji cen złota to statystyki dotyczące sytuacji ekonomicznej, gospodarczej i społecznej na świecie na przestrzeni lat oraz wskaźniki kapitalizacji giełdowej S&P Composite. Z powodu dużej ilości wartości nieznanych oraz faktu, że jeden z najpopularniejszych i najczęściej zalecanych algorytmów - Random Forest nie jest w stanie ich obsłużyć, konieczne były pewne modyfikacje. Z końcowego zbioru danych zostały usunięte parametry zawierające ponad połowę wartości pustych, natomiast akcja obsługi wartości pustych została ustawiona na na.roughfix, co polega na zastąpieniu nieznanych wartości medianą kolumny. Dzięki temu, jesteśmy w stanie oszacować najbardziej wpływowe czynniki, które są powiązane ze zmianą cen złota. W dużej mierze są to parametry, które pośrednio wskazują na upływ lat i starzenie się społeczeństwa (jak np odsetek populacji w wieku 65 lat i powyżej), ale również ogólne wskaźniki poziomu i stylu życia społeczeństwa (jak np PKB, odsetek urodzeń czy odsetek populacji miejskiej).

Analiza wskaźników kapitalizacji giełdowej w ostatnim stuleciu

Wskaźniki kapitalizacji giełdowej - zmiana wartości w przeciągu lat

s_p_composite <- read.csv("Dokumenty/ZED-lab/S&P Composite.csv", header=TRUE) %>%
                  gather(key = "param", value = "value", 2:10) %>%
                  mutate(year = as.numeric(substr(Year,1,4)))

s_p_composite <- aggregate(value ~ year + param, data = s_p_composite, mean)

s_p_no_missing_values <- s_p_composite[!is.na(s_p_composite$value), ]
s_p_composite_plot <- ggplot(data = s_p_no_missing_values, mapping = aes(x = year, y = value, color=param)) + geom_line() + geom_point() + facet_wrap(~param, scales = "free", ncol=1)

ggplotly(s_p_composite_plot)

Statystyki ogólne

st(s_p_composite %>% spread(param, value))
Summary Statistics
Variable N Mean Std. Dev. Min Pctl. 25 Pctl. 75 Max
year 151 1946 43.734 1871 1908.5 1983.5 2021
CPI 151 62.619 76.75 6.462 10.212 101.742 267.817
Cyclically.Adjusted.PE.Ratio 141 17.237 6.994 5.311 12.003 20.774 42.068
Dividend 151 6.901 12.434 0.18 0.422 7.14 59.094
Earnings 151 15.76 29.629 0.206 0.57 14.968 134.917
Long.Interest.Rate 151 4.501 2.295 0.894 3.219 5.049 13.911
Real.Dividend 151 17.637 11.373 5.728 9.365 22.489 62.339
Real.Earnings 151 35.241 30.244 7.871 14.529 43.987 144.072
Real.Price 151 625.906 742.279 84.791 186.284 711.639 4172.503
S.P.Composite 151 332.147 696.689 3.136 7.894 160.446 4114.705

Rozkład gęstości

s_p_composite_density_plot <- ggplot(s_p_composite, aes(x=value)) + geom_density() + facet_wrap(~param, ncol=1, scales = "free")

ggplotly(s_p_composite_density_plot)

Statystyki ogólne - forma wykresów pudełkowych - skala szczegółowa

s_p_composite_boxplot_multiple <- ggplot(data=s_p_composite, mapping = aes(x = as.factor(''), y=value, color = param)) + geom_boxplot() + facet_wrap(~param, scales = "free")
ggplotly(s_p_composite_boxplot_multiple)

Parametry związane z obrotem bitcoina

Zmiana wartości w przeciągu ostatnich lat

bchain_diff <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-DIFF.csv") %>%
                rename(diff = Value)

bchain_hrate <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-HRATE.csv") %>%
                rename(hrate = Value)

bchain_mkpru <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-MKPRU.csv") %>%
                rename(mkpru = Value)

bchain_trvou <- read.csv("Dokumenty/ZED-lab/Bitcoin/BCHAIN-TRVOU.csv") %>%
                rename(trvou = Value)

bchain <- merge(x = bchain_diff, y = bchain_hrate, by = "Date")
bchain <- merge(x = bchain, y = bchain_mkpru, by = "Date")
bchain <- merge(x = bchain, y = bchain_trvou, by = "Date")

bchain <- bchain %>% gather(key="param", value="value", 2:5)

bchain$Date <- substr(bchain$Date,1,4)
bchain <- bchain %>%
                mutate_at("Date", as.numeric)
bchain <- aggregate(bchain, by=list(bchain$Date, bchain$param), FUN=mean, na.rm=TRUE) %>%
            select(-Date, -param) %>%
            rename(Year = Group.1, param = Group.2)

chart_bchain <- ggplot(data=bchain, mapping = aes(x=Year, y=value)) + geom_point() + geom_line() + facet_wrap(~param, ncol=1, scales = "free")
ggplotly(chart_bchain)

Rozkład gęstości

bchain_density_plot <- ggplot(bchain, aes(x=value)) + geom_density() + facet_wrap(~param, ncol=1, scales = "free")

ggplotly(bchain_density_plot)

Statystyki ogólne

st(bchain %>% spread(param, value))
Summary Statistics
Variable N Mean Std. Dev. Min Pctl. 25 Pctl. 75 Max
Year 13 2015 3.894 2009 2012 2018 2021
diff 13 3957786186336.31 6889348202952.88 0.987 2125246.06 4970320545967.22 19789690360642.1
hrate 13 28521852.776 49255677.911 0 15.702 36394798.569 140094812.581
mkpru 13 5854.036 12216.739 0 8.474 7362.713 44566.454
trvou 13 155486175.602 233643226.415 0 298104.978 189145596.269 626774501.417

Statystyki ogólne - forma wykresów pudełkowych - wspólna skala

bchain_boxplot <- ggplot(data=bchain, mapping = aes(y=value, color=param)) + geom_boxplot()
ggplotly(bchain_boxplot)

Statystyki ogólne - forma wykresów pudełkowych - skala szczegółowa

bchain_boxplot_multiple <- ggplot(data=bchain, mapping = aes(x = as.factor(''), y=value, color = param)) + geom_boxplot() + facet_wrap(~param, scales = "free") + ylab("Params boxplots")
ggplotly(bchain_boxplot_multiple)

Wartości walut w odniesieniu do dolara amerykańskiego

Zmiana na przestrzeni lat - wspólna skala

currency_exchange_rates <- read.csv("Dokumenty/ZED-lab/CurrencyExchangeRates.csv", header=TRUE)
currency_exchange_rates <- currency_exchange_rates %>% gather(key = "currency", value = "value", 2:52)
currency_exchange_rates$Date <- substr(currency_exchange_rates$Date,1,4)
currency_exchange_rates <- aggregate(currency_exchange_rates, by=list(currency_exchange_rates$Date, currency_exchange_rates$currency), FUN=mean, na.rm=TRUE) %>% select(-Date, -currency) %>% rename(Year = Group.1, Currency = Group.2)

currency_exchange_no_missing_values <- currency_exchange_rates[!is.na(currency_exchange_rates$value), ]

currency_exchange_rates_plot <- ggplot(data=currency_exchange_no_missing_values, mapping = aes(x=factor(Year), y=value, color=Currency)) + geom_point() + geom_line(aes(group=1)) + scale_x_discrete(breaks = seq(1995, 2018, by = 5))

ggplotly(currency_exchange_rates_plot)

Zmiana na przestrzeni lat - skala szczegółowa

currency_facet_plot <- ggplot(data=currency_exchange_no_missing_values, mapping = aes(x = factor(Year), y = value, color=Currency)) + geom_line(aes(group=1)) + geom_point() + facet_wrap(~Currency, ncol=1, scales = "free") + scale_x_discrete(breaks = seq(1995, 2018, by = 5))

ggplotly(currency_facet_plot)

Statystyki ogólne

st(currency_exchange_rates %>% spread(Currency, value))
Summary Statistics
Variable N Mean Std. Dev. Min Pctl. 25 Pctl. 75 Max
Algerian.Dinar 9 91.154 17.248 72.841 77.636 109.451 114.14
Australian.Dollar 24 0.77 0.137 0.518 0.715 0.842 1.036
Bahrain.Dinar 24 0.376 0 0.376 0.376 0.376 0.376
Bolivar.Fuerte 11 2498.607 8269.223 2.145 3.434 7.832 27431.25
Botswana.Pula 21 0.276 0.57 0.092 0.119 0.197 2.758
Brazilian.Real 24 2.193 0.767 0.916 1.796 2.928 3.488
Brunei.Dollar 21 1.502 0.193 1.25 1.366 1.69 1.792
Canadian.Dollar 24 1.265 0.183 0.989 1.097 1.389 1.57
Chilean.Peso 24 542.18 82.576 401.985 486.722 603.898 691.235
Chinese.Yuan 24 7.43 0.895 6.143 6.598 8.277 8.374
Colombian.Peso 24 2106.72 593.404 913.61 1833.347 2534.417 3050.151
Czech.Koruna 18 23.193 5.41 17.032 19.565 24.563 38.022
Danish.Krone 24 6.273 0.878 5.099 5.62 6.705 8.318
Euro 21 1.207 0.161 0.896 1.109 1.328 1.471
Hungarian.Forint 21 229.96 36.276 171.799 202.26 258.345 286.458
Icelandic.Krona 24 92.96 24.218 62.837 70.775 117.732 131.896
Indian.Rupee 24 48.753 9.823 32.397 43.343 54.733 67.197
Indonesian.Rupiah 21 9362.046 3357.447 2248.032 8932.033 11323.333 13632.608
Iranian.Rial 24 12125.235 11130.616 1747.884 1753.242 13799.936 38039.113
Israeli.New.Sheqel 18 3.984 0.407 3.48 3.602 4.397 4.738
Japanese.Yen 24 107.881 13.471 79.771 101.969 116.665 131.081
Kazakhstani.Tenge 14 188.241 81.283 120.294 137.207 210.222 341.906
Korean.Won 24 1101.035 145.608 770.901 1045.34 1167.018 1394.534
Kuwaiti.Dinar 24 0.294 0.011 0.269 0.286 0.303 0.307
Libyan.Dinar 24 1.524 0.616 0.525 0.727 1.932 1.932
Malaysian.Ringgit 24 3.516 0.478 2.508 3.203 3.8 4.299
Mauritian.Rupee 17 31 2.432 27.528 29.513 31.909 35.525
Mexican.Peso 21 12.235 3.472 6.511 10.797 13.3 18.862
Nepalese.Rupee 24 78.063 15.709 51.884 69.344 87.436 107.452
New.Zealand.Dollar 24 0.663 0.113 0.421 0.622 0.722 0.831
Norwegian.Krone 24 6.987 1.057 5.61 6.224 7.878 8.989
Nuevo.Sol 9 2.975 0.281 2.637 2.753 3.235 3.371
Pakistani.Rupee 24 71.683 24.597 31.708 56.506 95.205 112.236
Peso.Uruguayo 10 22.755 6.231 9.32 20.379 28.127 30.065
Philippine.Peso 13 41.015 8.555 25.835 41.072 45.499 51.598
Polish.Zloty 22 3.305 0.496 2.405 3.039 3.742 4.101
Qatar.Riyal 24 3.64 0 3.64 3.64 3.64 3.64
Rial.Omani 24 0.384 0 0.384 0.384 0.384 0.384
Russian.Ruble 16 37.65 14.433 24.869 28.694 43.435 66.849
Saudi.Arabian.Riyal 24 3.749 0.002 3.745 3.748 3.75 3.75
Singapore.Dollar 24 1.495 0.183 1.25 1.372 1.677 1.792
South.African.Rand 24 8.22 2.891 3.627 6.447 9.861 14.702
Sri.Lanka.Rupee 24 104.077 30.161 51.264 85.972 128 155.171
Swedish.Krona 24 7.741 1.028 6.495 6.841 8.309 10.326
Swiss.Franc 24 1.2 0.25 0.888 0.979 1.372 1.69
Thai.Baht 24 35.092 5.245 24.919 31.463 40.181 44.484
Trinidad.And.Tobago.Dollar 24 6.324 0.193 5.893 6.262 6.388 6.756
Tunisian.Dinar 9 1.857 0.399 1.407 1.562 2.142 2.436
U.A.E..Dirham 24 3.672 0.001 3.671 3.672 3.672 3.673
U.K..Pound.Sterling 24 1.607 0.168 1.289 1.526 1.65 2.002
U.S..Dollar 24 1 0 1 1 1 1

Ceny złota

Zmiana na przestrzeni lat

gold_prices <- read.csv("Dokumenty/ZED-lab/Gold prices.csv", header=TRUE) %>%
                gather(key = "currency", value = "value", 2:7) %>%
                mutate(year = as.numeric(substr(Date,1,4))) %>%
                select(-Date)
gold_prices <- aggregate(value ~ year + currency, data = gold_prices, mean)
                
gold_prices_plot <- ggplot(data = gold_prices, mapping = aes(x = year, y = value, color = currency)) + geom_line() + geom_point()

ggplotly(gold_prices_plot)

Statystyki ogólne

st(gold_prices %>% spread(currency, value))
Summary Statistics
Variable N Mean Std. Dev. Min Pctl. 25 Pctl. 75 Max
year 54 1994.5 15.732 1968 1981.25 2007.75 2021
EURO..AM. 23 805.093 424.121 261.634 344.145 1121.539 1549.454
EURO..PM. 23 804.846 424.125 261.367 343.69 1121.811 1548.864
GBP..AM. 54 374.948 359.867 15.012 179.256 441.393 1379.374
GBP..PM. 54 374.769 359.748 15.006 179.158 440.367 1378.841
USD..AM. 54 580.957 498.162 35.964 282.998 828.387 1801.001
USD..PM. 54 580.693 497.976 35.95 282.848 827.819 1800.02

Statystyki ogólne - forma wykresów pudełkowych - skala szczegółowa

gold_prices_boxplot_multiple <- ggplot(data=gold_prices, mapping = aes(x = as.factor(''), y=value, color = currency)) + geom_boxplot() + facet_wrap(~currency, scales = "free", ncol = 2)
ggplotly(gold_prices_boxplot_multiple)

Rozwój państw

Heatmapa korelacji parametrów określających rozwój kraju

wdi <- read_excel("Dokumenty/ZED-lab/World_Development_Indicators.xlsx") %>%
  data.frame() %>%
  filter(!is.na(Country.Code))

wdi[wdi == ".."] <- NA

wdi <- wdi %>%
  gather("year","value", -Country.Name, -Country.Code, -Series.Name, -Series.Code) %>%
  mutate_at("value", as.numeric)

wdi$year <- substr(wdi$year,2,5)
#wdi_by_country <- wdi %>%
#                  mutate(ID = paste(Country.Code,year)) %>%
#                  spread(Series.Code,value) %>%
#                  select(-year)

wdi_by_year_only <- wdi %>% 
                    spread(Series.Name,value) %>%
                    select(-Country.Code, -Series.Code, -Country.Name)
wdi_by_year_only <- aggregate(wdi_by_year_only, by=list(wdi_by_year_only$year), FUN=mean, na.rm = TRUE) %>%
                    select(-year) %>%
                    rename(year = Group.1)

wdi_by_year_only$year <- as.numeric(wdi_by_year_only$year)
                    

#wdi <- aggregate(wdi, by=list(wdi$ID), FUN=mean, na.rm=TRUE) %>%
#  select(-Group.1, -Country.Name, -Country.Code, -Series.Name, -ID)



#col<- colorRampPalette(c("blue", "white", "red"))(20)

#heatmap(x = wdi.cor, col = col, symm = TRUE)
gold_prices <- gold_prices %>% filter(currency == 'USD..AM.') %>% select(-currency) %>% rename(gold_price = value)

corr_data <- merge(x = wdi_by_year_only, y = gold_prices, by = "year")
corr_data <- merge(x=corr_data, y = s_p_composite %>% spread(param, value), by = "year") %>%
                          select(-year) 
cor <- cor(corr_data, use = "pairwise.complete.obs")

Heatmapa korelacji - interaktywna

heatmaply_cor(cor)

Najwyższe korelacje

cor_table <- as.data.frame(as.table(cor))

biggest_corrs <- subset(cor_table, abs(Freq) > 0.9) %>%
                  filter(abs(Freq) < 1) %>%
                  arrange(desc(Freq))

Przewidywanie ceny złota przy użyciu Random Forest

#corr_data$gold_price <- as.factor(corr_data$gold_price)

set.seed(0)
corr_data <- corr_data[, which(colMeans(!is.na(corr_data)) > 0.5)]
split <- createDataPartition(corr_data$gold_price, p = 0.8, list = FALSE)

training <- corr_data[split,]
testing <- corr_data[-split,]

rfGrid <- expand.grid(mtry = 10:30)
control <- trainControl(method = "cv", number = 10)
rfFitTune <- train(gold_price ~ .,
                   data = training,
                   method = "rf",
                   trControl = control,
                   tuneGrid = rfGrid,
                   na.action = na.roughfix,
                   ntree = 30)
rfPred <- predict(rfFitTune , testing)

knitr::kable(postResample(pred = rfPred, obs = testing$gold_price))
## Warning in pred - obs: długość dłuszego obiektu nie jest wielokrotnością
## długości krótszego obiektu

## Warning in pred - obs: długość dłuszego obiektu nie jest wielokrotnością
## długości krótszego obiektu
x
RMSE 597.5772
Rsquared NA
MAE 342.6423

Waga atrybutów

most_important_params <- arrange(varImp(rfFitTune)$importance, desc(Overall))
knitr::kable(most_important_params)
Overall
Population ages 65 and above (% of total population) 100.0000000
Primary school starting age (years) 80.8563320
Population density (people per sq. km of land area) 53.4587993
Net domestic credit (current LCU) 52.6590768
Taxes on exports (current LCU) 48.9759980
Population in largest city 48.0025427
Rural population 41.0611870
Population in the largest city (% of urban population) 37.6692712
Population, male 37.2659936
Gross domestic savings (current US$) 36.2706494
Urban population (% of total population) 35.1373015
GDP (current US$) 33.8950079
Birth rate, crude (per 1,000 people) 33.8443819
Population, male (% of total population) 32.5771095
Population, female 32.3449556
Population ages 0-14 (% of total population) 31.6577552
Net primary income (Net income from abroad) (current LCU) 27.5124392
Long.Interest.Rate 26.7542316
Employment in services (% of total employment) (modeled ILO estimate) 26.1037024
CPI 25.3032190
Pupil-teacher ratio, primary 24.5393840
Imports of goods and services (current US$) 22.1230091
Urban population growth (annual %) 5.6790936
Service imports (BoP, current US$) 5.3768187
Mortality rate, infant (per 1,000 live births) 5.1951032
Unemployment, total (% of total labor force) (national estimate) 4.8525022
Rural population (% of total population) 4.7604971
Access to electricity (% of population) 4.5272803
CO2 emissions (kg per 2017 PPP $ of GDP) 3.8979282
Population growth (annual %) 3.4443390
Gross national expenditure (current US$) 3.4015567
Pupil-teacher ratio, preprimary 3.2125578
Population, female (% of total population) 3.1409353
Lending interest rate (%) 3.1264868
Trade in services (% of GDP) 2.9771743
CO2 emissions (kg per 2010 US$ of GDP) 2.8247200
Transport services (% of commercial service exports) 2.7315317
Short-term debt (% of total reserves) 2.6961226
Population in urban agglomerations of more than 1 million 2.6911832
Exports of goods and services (current US$) 2.3995592
Trademark applications, direct resident 2.3926987
Secondary education, teachers 2.3431786
Net acquisition of financial assets (% of GDP) 2.2098728
Taxes less subsidies on products (current LCU) 2.1910271
Patent applications, nonresidents 2.1796672
Employment in agriculture (% of total employment) (modeled ILO estimate) 2.1567295
Electricity production from nuclear sources (% of total) 2.0904594
Net primary income (Net income from abroad) (constant LCU) 1.9632592
CO2 intensity (kg per kg of oil equivalent energy use) 1.9289845
Interest payments (% of expense) 1.8619920
Imports of goods and services (% of GDP) 1.8421177
Net primary income (Net income from abroad) (current US$) 1.8089223
Pupil-teacher ratio, upper secondary 1.7212088
CO2 emissions from manufacturing industries and construction (% of total fuel combustion) 1.7011348
Pupil-teacher ratio, tertiary 1.6658654
Individuals using the Internet (% of population) 1.6390809
Portfolio investment, bonds (PPG + PNG) (NFL, current US$) 1.5924655
Gross national expenditure (% of GDP) 1.5190008
Taxes on income, profits and capital gains (% of revenue) 1.3958916
Survival to age 65, male (% of cohort) 1.3778093
Trademark applications, direct nonresident 1.3613775
Deposit interest rate (%) 1.3581518
Total natural resources rents (% of GDP) 1.3432132
Service exports (BoP, current US$) 1.3173941
Total greenhouse gas emissions (kt of CO2 equivalent) 1.3158992
Self-employed, total (% of total employment) (modeled ILO estimate) 1.2807738
Self-employed, male (% of male employment) (modeled ILO estimate) 1.2583799
Urban population 1.1917421
Natural gas rents (% of GDP) 1.1894019
GDP per capita (current US$) 1.1338543
Primary income receipts (BoP, current US$) 1.0131950
Fuel imports (% of merchandise imports) 0.9454639
Manufacturing, value added (% of GDP) 0.9150827
Taxes on income, profits and capital gains (% of total taxes) 0.9039033
Real interest rate (%) 0.8409638
Electricity production from hydroelectric sources (% of total) 0.7705298
Trade (% of GDP) 0.7529782
Taxes on international trade (% of revenue) 0.6953153
School enrollment, tertiary (gross), gender parity index (GPI) 0.6749071
Self-employed, female (% of female employment) (modeled ILO estimate) 0.6441500
Electricity production from renewable sources, excluding hydroelectric (kWh) 0.6166715
Gross domestic savings (% of GDP) 0.5374528
Taxes on international trade (current LCU) 0.5279232
Short-term debt (% of total external debt) 0.4550172
Methane emissions in energy sector (thousand metric tons of CO2 equivalent) 0.4499030
Patent applications, residents 0.4116928
External debt stocks (% of GNI) 0.3804283
Share of youth not in education, employment or training, total (% of youth population) 0.3759905
S.P.Composite 0.3546983
Food exports (% of merchandise exports) 0.3286586
Taxes on goods and services (current LCU) 0.2735620
Tax revenue (% of GDP) 0.2619465
CO2 emissions from solid fuel consumption (kt) 0.2567704
CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion) 0.2468928
Population ages 15-64 (% of total population) 0.2458792
Short-term debt (% of exports of goods, services and primary income) 0.2289043
GDP per capita growth (annual %) 0.2269565
Taxes on goods and services (% of revenue) 0.2118874
Cyclically.Adjusted.PE.Ratio 0.1937372
Number of under-five deaths 0.1764525
Trademark applications, total 0.1732389
Taxes on exports (% of tax revenue) 0.1628117
Real.Earnings 0.1574525
CO2 emissions from transport (% of total fuel combustion) 0.1419731
CO2 emissions from liquid fuel consumption (kt) 0.1418419
Life expectancy at birth, total (years) 0.1410901
Literacy rate, adult total (% of people ages 15 and above) 0.1212869
Electricity production from renewable sources, excluding hydroelectric (% of total) 0.1102551
CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion) 0.0976779
Stocks traded, total value (% of GDP) 0.0932381
CO2 emissions (kt) 0.0806734
CO2 emissions from gaseous fuel consumption (% of total) 0.0803164
Methane emissions (kt of CO2 equivalent) 0.0785596
Gross savings (current US$) 0.0742113
Earnings 0.0658151
GNI growth (annual %) 0.0623997
Goods imports (BoP, current US$) 0.0574973
Renewable electricity output (% of total electricity output) 0.0562162
CO2 emissions (metric tons per capita) 0.0560265
Dividend 0.0555865
Taxes on goods and services (% value added of industry and services) 0.0555147
Goods exports (BoP, current US$) 0.0476203
Net primary income (BoP, current US$) 0.0442267
Taxes less subsidies on products (constant LCU) 0.0430038
Share of youth not in education, employment or training, male (% of male youth population) 0.0370544
Net official development assistance received (current US$) 0.0313795
Income share held by highest 10% 0.0290213
Inflation, consumer prices (annual %) 0.0277853
Part time employment, total (% of total employment) 0.0272075
Stocks traded, turnover ratio of domestic shares (%) 0.0234348
Primary income payments (BoP, current US$) 0.0233998
Portfolio investment, net (BoP, current US$) 0.0219749
Electricity production from coal sources (% of total) 0.0210960
Real.Price 0.0196614
Merchandise exports to high-income economies (% of total merchandise exports) 0.0134215
CO2 emissions from gaseous fuel consumption (kt) 0.0102393
S&amp;P Global Equity Indices (annual % change) 0.0095169
CO2 emissions from liquid fuel consumption (% of total) 0.0056824
Services, value added (% of GDP) 0.0054789
Land area (sq. km) 0.0048623
Taxes less subsidies on products (current US$) 0.0045670
Taxes on income, profits and capital gains (current LCU) 0.0045075
Electricity production from natural gas sources (% of total) 0.0033883
Portfolio equity, net inflows (BoP, current US$) 0.0026918
Tax revenue (current LCU) 0.0019399
Secondary education, pupils 0.0001423
CO2 emissions from electricity and heat production, total (% of total fuel combustion) 0.0001411
CO2 emissions (kg per PPP $ of GDP) 0.0000000
CO2 emissions from solid fuel consumption (% of total) 0.0000000
Consumer price index (2010 = 100) 0.0000000
Electricity production from oil, gas and coal sources (% of total) 0.0000000
Employers, total (% of total employment) (modeled ILO estimate) 0.0000000
Employment in industry (% of total employment) (modeled ILO estimate) 0.0000000
Expense (% of GDP) 0.0000000
Exports of goods and services (annual % growth) 0.0000000
Food imports (% of merchandise imports) 0.0000000
Fuel exports (% of merchandise exports) 0.0000000
GDP growth (annual %) 0.0000000
Government expenditure on education, total (% of GDP) 0.0000000
Gross savings (% of GDP) 0.0000000
Labor force, total 0.0000000
Nitrous oxide emissions (thousand metric tons of CO2 equivalent) 0.0000000
Nitrous oxide emissions in energy sector (% of total) 0.0000000
Population, total 0.0000000
Pupil-teacher ratio, secondary 0.0000000
Renewable energy consumption (% of total final energy consumption) 0.0000000
Rural population growth (annual %) 0.0000000
Share of youth not in education, employment or training, female (% of female youth population) 0.0000000
Stocks traded, total value (current US$) 0.0000000
Survival to age 65, female (% of cohort) 0.0000000
Transport services (% of commercial service imports) 0.0000000
Unemployment with advanced education (% of total labor force with advanced education) 0.0000000
Real.Dividend 0.0000000

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.